基于数字孪生和PSO算法的混动车辆能量管理策略鲁棒优化

Robust optimization of energy management strategy in hybrid vehicles based on digital twin and PSO algorithm

该文针对混动车辆能量管理策略开发任务,基于车辆数字孪生平台,提出了一种融合全局交叉验证和粒子群优化(PSO)的鲁棒优化算法,以获得高可靠性、适应性的能量管理策略。基于转鼓台架试验结果建立了某混动车辆数字孪生模型,定义了综合考虑车辆能量转换效率和电池剩余电量的控制效用指标,搭建了基于自适应神经模糊推理系统(ANFIS)能量管理控制器;利用粒子群鲁棒优化算法在JC08、WLTC、UDDS等国际常用行驶工况对控制器进行超参数优化,并基于硬件在环平台对优化结果进行了对比验证。结果表明:通过综合考虑训练工况和验证工况下的控制效用,粒子群鲁棒优化算法相比标准粒子群算法,能够提升11%以上的控制效用值,获得0.41%至27.92%的燃油经济性提升。

A robust particle swarm optimization(PSO)scheme for the development of energy management strategy for hybrid vehicles was proposed based on digital twin.By incorporating global cross-validation with local particle swarm optimization,the proposed scheme aimed to achieve more reliable optimization for energy management.First,a digital twin model for the hybrid vehicle was built based on the chassis dynamometer test data and an adaptive neural fuzzy inference system(ANFIS)was then developed for real-time energy management.By introducing the concept of control utility,which evaluated the vehicle energy efficiency with a penalty factor of battery usage,the robust particle swarm optimisation scheme was deployed to optimize the hyper parameters of the ANFIS controller.The optimization performances were evaluated through experiment based on the hardware-in-the-loop testing platform under worldwide driving cycles including JC08,WLTC,and UDDS.Compared to conventional particle swarm optimisation,the proposed robust particle swarm optimization can achieve more than 11%higher control utility value in both learning cycles and testing cycles and improve the fuel economy by up to 27.92%.